Analysis of noisy survival data with graphical proportional hazards measurement error models

In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data wit...

Full description

Saved in:
Bibliographic Details
Published in:Biometrics 2021-09, Vol.77 (3), p.956-969
Main Authors: Chen, Li‐Pang, Yi, Grace Y.
Format: Article
Language:English
Subjects:
Data analysis
Error analysis
error‐prone variable
graphical model
Hazards
high‐dimensionality
Numerical methods
simulation‐extrapolation
Survival
survival analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High‐dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error‐contaminated. To hand such kinds of survival data, we propose graphical PH measurement error models and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.13331